Name
Title | ||
|---|---|---|
Con-Cname: A Contextual Multi-Armed Bandit Algorithm For Personalized Recommendations |
Abstract | ||
|---|---|---|
Reinforcement learning algorithms play an important role in modern day and have been applied to many domains. For example, personalized recommendations problem can be modelled as a contextual multi-armed bandit problem in reinforcement learning. In this paper, we propose a contextual bandit algorithm which is based on Contexts and the Chosen Number of Arm with Minimal Estimation, namely Con-CNAME in short. The continuous exploration and context used in our algorithm can address the cold start problem in recommender systems. Furthermore, the Con-CNAME algorithm can still make recommendations under the emergency circumstances where contexts are unavailable suddenly. In the experimental evaluation, the reference range of key parameters and the stability of Con-CNAME are discussed in detail. In addition, the performance of Con-CNAME is compared with some classic algorithms. Experimental results show that our algorithm outperforms several bandit algorithms. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01421-6_32 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT II |
Keywords | Field | DocType |
Recommender systems, Reinforcement learning, Multi-armed bandit, Context-aware | Recommender system,Cold start,Computer science,Algorithm,Multi-armed bandit,Artificial intelligence,CNAME record,Machine learning,Reinforcement learning | Conference |
Volume | ISSN | Citations |
11140 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaofang Zhang | 1 | 11 | 4.82 |
| Qian ZHOU | 2 | 36 | 13.44 |
| Tieke He | 3 | 58 | 15.85 |
| bin liang | 4 | 20 | 5.19 |